Many clinical trials compare a new treatment with a control on multiple endpoints. Protocols often specify complex decision rules that involve a combination of superiority and equivalence (non-inferiority) tests on primary, co-primary and secondary endpoints; some tests may be conditional on the outcomes of the other tests. Also, it is not uncommon to find sponsors proposing an adaptive modification of the protocol rule. Statistical properties of such rules are generally unknown and often they do not control the desired alpha level. This leads to much debate and confusion in the statistical evaluation of drug trials data. The long-term goal of the proposed research is to provide a general systematic framework for analyzing such rules and determining optimal allocation of the desired alpha level among the component tests to maximize power. Two approaches will be explored in achieving the research goals, both related to the theory of multiple comparisons. The first approach uses union-intersection and intersection-union tests, and assumes multivariate normality. The second approach uses the theory of gatekeeping procedures, and generalizes parallel and serial gatekeeping methods to what we call as multiple tree gatekeeping procedures. This approach is based on p-values of component test statistics and does not assume any particular distribution for the data. Bootstrap methodology will be used to implement the tests in many situations because of unknown correlations among the endpoints or non-normality of the data. The primary public health benefit of the proposed research is that it will facilitate correct statistical evaluation of multiple endpoints data. In particular, it will prevent approval of an inefficacious drug or a treatment which may be shown to be effective if incorrect statistical methods are used that do not properly control the type I error inflation caused by multiple tests on the endpoints. [unreadable] [unreadable] [unreadable]